Controlling an Anatomical Robot Hand Using the Brain-Computer Interface Based on Motor Imagery

Abstract

More than one billion people face disabilities worldwide, according to the World Health Organization (WHO). In Sri Lanka, there are thousands of people suffering from a variety of disabilities, especially hand disabilities, due to the civil war in the country. The Ministry of Health of Sri Lanka reports that by 2025, the number of people with disabilities in Sri Lanka will grow by 24.2%. In the field of robotics, new technologies for handicapped people are now being built to make their lives simple and effective. The aim of this research is to develop a 3-finger anatomical robot hand model for handicapped people and control (flexion and extension) the robot hand using motor imagery. Eight EEG electrodes were used to extract EEG signals from the primary motor cortex. Data collection and testing were performed for a period of 42 s timespan. According to the test results, eight EEG electrodes were sufficient to acquire the motor imagery for flexion and extension of finger movements. The overall accuracy of the experiments was found at 89.34% (mean = 22.32) at the 0.894 precision. We also observed that the proposed design provided promising results for the performance of the task (grab, hold, and release activities) of hand-disabled persons.

Object Weight Perception in Motor Imagery Using Fourier-Based Synchrosqueezing Transform and Regularized Common Spatial Patterns

“…They employ the EEGNet, a convolutional neural network, for feature extraction and signal classification across five motorimagery hand tasks performed by users. In [21], 3-finger anatomical robot hand model is developed for handicapped people. Eight channel motor imagery EEG data from primary motor cortex is used to control (flexion and extension) the robot hand.…”

Early-stage fusion of EEG and fNIRS improves classification of motor imagery

“…These years have witnessed thriving progress in the field of BCI. Motor imagery (MI) is one of the common paradigms in BCI research (Kaiser et al, 2011 ), which is accomplished by imagining performing the given task (Jeannerod, 1995 ), such as grabbing (Herath and Mel, 2021 ), lifting (Kasemsumran and Boonchieng, 2019 ), and so on. MI-BCIs are widely used to aid patients with motor function impairments caused by stroke (Ang et al, 2010 ), amyotrophic lateral sclerosis (Lulé et al, 2007 ), spinal cord injury (Cramer et al, 2007 ), and so on, either for daily-life assistance or rehabilitative training.…”

Impact of swiping direction on the interaction performance of elderly-oriented smart home interface: EEG and eye-tracking evidence

“…With the rapid development of cognitive neuroscience (Seitamaa-Hakkarainen et al, 2016 ; Chrysikou and Gero, 2020 ; Slagter and Bouwer, 2021 ), more and more scholars are considering the use of cognitive neural techniques for evaluating human-computer interaction performance. One of these, the Electroencephalogram (EEG), has proven to be a reliable indicator of spontaneous brain activity (Gevins et al, 1997 ; Stikic et al, 2011 ; Herath and de Mel, 2021 ). In Chen et al ( 2017 ), one electroencephalographic (EEG) method was proposed based on primary band power spectral density (PSD) to assess brain load tasks, and indicated that the channel in the left frontoparietal lobe (Fp1) had the highest correlation with brain load levels; In Kumar and Kumar ( 2016 ), the authors demonstrated that the average power in each band can be used for the characterization of cognitive load; In Ismail and Karwowski ( 2020 ), the authors summarized the research on EEG in the cognitive domain and indicated that the amplitude of some ERP components (e.g., P3, P2, N1, N2) decreased with increasing workload.…”